"... On the basis of velocity clues alone": some perceptual themes 1946-1996

Optic Flow: A History

The concept of optic flow, a global pattern of visual motion that is both caused by and signals self-motion, is canonically ascribed to James Gibson's 1950 book "The Perception of the Visual World." There have, however, been several other developments of this concept, chiefly by Gwilym Grindley and Edward Calvert. Based on rarely referenced scientific literature and archival research, this article describes the development of the concept of optic flow by the aforementioned authors and several others. The article furthermore presents the available evidence for interactions between these authors, focusing on whether parts of Gibson's proposal were derived from the work of Grindley or Calvert. While Grindley's work may have made Gibson aware of the geometrical facts of optic flow, Gibson's work is not derivative of Grindley's. It is furthermore shown that Gibson only learned of Calvert's work in 1956, almost a decade after Gibson first published his proposal. In conclusion, the development of the concept of optic flow presents an intriguing example of convergent thought in the progress of science.

Information Is Where You Find It: Perception as an Ecologically Well-Posed Problem

Texts on visual perception typically begin with the following premise: Vision is an ill-posed problem, and perception is underdetermined by the available information. If this were really the case, however, it is hard to see how vision could ever get off the ground. James Gibson's signal contribution was his hypothesis that for every perceivable property of the environment, however subtle, there must be a higher order variable of information, however complex, that specifies it-if only we are clever enough to find them. Such variables are informative about behaviorally relevant properties within the physical and ecological constraints of a species' niche. Sensory ecology is replete with instructive examples, including weakly electric fish, the narwal's tusk, and insect flight control. In particular, I elaborate the case of passing through gaps. Optic flow is sufficient to control locomotion around obstacles and through openings. The affordances of the environment, such as gap passability, are specified by action-scaled information. Logically ill-posed problems may thus, on closer inspection, be ecologically well-posed.

Optic Flow: Perceiving and Acting in a 3-D World

In 1979, James Gibson completed his third and final book "The Ecological Approach to Visual Perception". That book can be seen as the synthesis of the many radical ideas he proposed over the previous 30 years - the concept of information and its sufficiency, the necessary link between perception and action, the need to see perception in relation to an animal's particular ecological niche and the meanings (affordances) offered by the visual world. One of the fundamental concepts that lies beyond all of Gibson's thinking is that of optic flow: the constantly changing patterns of light that reach our eyes and the information it provides. My purpose in writing this paper has been to evaluate the legacy of Gibson's conceptual ideas and to consider how his ideas have influenced and changed the way we study perception.

The Primary Role of Flow Processing in the Identification of Scene-Relative Object Movement

Retinal image motion could be due to the movement of the observer through space or an object relative to the scene. Optic flow, form, and change of position cues all provide information that could be used to separate out retinal motion due to object movement from retinal motion due to observer movement. In Experiment 1, we used a minimal display to examine the contribution of optic flow and form cues. Human participants indicated the direction of movement of a probe object presented against a background of radially moving pairs of dots. By independently controlling the orientation of each dot pair, we were able to put flow cues to self-movement direction (the point from which all the motion radiated) and form cues to self-movement direction (the point toward which all the dot pairs were oriented) in conflict. We found that only flow cues influenced perceived probe movement. In Experiment 2, we switched to a rich stereo display composed of 3D objects to examine the contribution of flow and position cues. We moved the scene objects to simulate a lateral translation and counter-rotation of gaze. By changing the polarity of the scene objects (from light to dark and vice versa) between frames, we placed flow cues to self-movement direction in opposition to change of position cues. We found that again flow cues dominated the perceived probe movement relative to the scene. Together, these experiments indicate the neural network that processes optic flow has a primary role in the identification of scene-relative object movement.

SIGNIFICANCE STATEMENT Motion of an object in the retinal image indicates relative movement between the observer and the object, but it does not indicate its cause: movement of an object in the scene; movement of the observer; or both. To isolate retinal motion due to movement of a scene object, the brain must parse out the retinal motion due to movement of the eye (“flow parsing”). Optic flow, form, and position cues all have potential roles in this process. We pitted the cues against each other and assessed their influence. We found that flow parsing relies on optic flow alone. These results indicate the primary role of the neural network that processes optic flow in the identification of scene-relative object movement.

Heading recovery from optic flow: comparing performance of humans and computational models

Human observers can perceive their direction of heading with a precision of about a degree. Several computational models of the processes underpinning the perception of heading have been proposed. In the present study we set out to assess which of four candidate models best captured human performance; the four models we selected reflected key differences in terms of approach and methods to modelling optic flow processing to recover movement parameters. We first generated a performance profile for human observers by measuring how performance changed as we systematically manipulated both the quantity (number of dots in the stimulus per frame) and quality (amount of 2D directional noise) of the flow field information. We then generated comparable performance profiles for the four candidate models. Models varied markedly in terms of both their performance and similarity to human data. To formally assess the match between the models and human performance we regressed the output of each of the four models against human performance data. We were able to rule out two models that produced very different performance profiles to human observers. The remaining two shared some similarities with human performance profiles in terms of the magnitude and pattern of thresholds. However none of the models tested could capture all aspect of the human data.